This dissertation studies generalized passive seismic monitoring techniques in both subsurface imaging and human activity monitoring applications. It has two parts; the first part designs the distributed computing of the traditional geophysical algorithm within a sensor network system. In traditional geophysical techniques, usually, the data acquisition is made in multiple measurement points separated by several hundred meters, over several hours to several days, using portable seismometers loggers that do not have real-time monitoring alert capability. By implementing the passive seismic monitoring and imaging algorithm in a distributed sensor network, the in-situ calculation and real-time results are becoming achievable. The distributed microseismic localization and ambient noise imaging algorithms are designed and demonstrated. The second part explores human activity monitoring based on the in-situ passive seismic monitoring principles. Specifically, the research considers human activity events as passive floor seismic sources and uses the geophysical algorithm to detect and analyze them. Also, machine learning approaches are adopted to track and identify more complex human activities. The system design is a non-invasive approach for activity monitoring and has essential home health and safety monitoring applications.